Theory for socio-demographic enrichment performance using the inverse discrete choice modelling approach

In light of the growing availability of big data sources and the essential role of socio-demographic information in travel behaviour and transport demand modelling more broadly, the enrichment of socio-demographic attributes for anonymous big datasets is a key issue that continues to be explored. The common shortcoming of existing socio-demographic enrichment approaches concerns their lack of consistent theory that can link their enrichment performance (i.e. the ability to correctly enrich the required attribute) to the underlying covariance structure in the anonymous big datasets. In other words, existing approaches are unable to indicate, prior to the enrichment, to what extent it will be successful. Instead, they require undertaking the enrichment itself to assess and validate it post factum, incurring the effort and cost of the activity. An alternative and arguably preferable way would be to have a prior indicator as to whether an enrichment is likely to be sufficiently effective for the desired application.